Dignite Paperbase

Paperbase = physical paper / scans / photos / PDF images / Office files → trustworthy digitized data. A channel layer, not an end-product. It doesn't consume, doesn't own, doesn't dive into business — it hands Markdown + structured metadata to downstream RAG platforms, business systems, and AI clients via REST / EventBus / MCP server / Webhook.

For the full positioning, architecture rules, OUT-of-scope list, Markdown-first contract, six-stage ETO event contract, and security covenant, see CLAUDE.md. It is the truth source — this README only stages the operational entry points.

Data flow

physical paper / scans / photos / PDF images / Office files
    ↓
[Paperbase channel]: OCR + Markdown + system metadata + type-bound field extraction
    ↓ (REST / EventBus / MCP server / Webhook)
    ├─→ downstream RAG platform
    ├─→ business systems (finance / CLM / HR / ERP)
    ├─→ AI clients (Claude Desktop / Cursor / any MCP client)
    └─→ any consumer (build your own subscriber)

Solution structure

dignite-paperbase/
├── core/      # Channel implementation — ABP layers (Abstractions / Domain.Shared / Domain / Application / EntityFrameworkCore / HttpApi)
├── host/      # Host application — provider wiring (OCR + AI) and middleware (ASP.NET Core API)
├── angular/   # Angular SPA (operator UI)
└── docs/      # Operator-facing documentation (design decisions go to GitHub Issues, not here)

Business modules (contract management / invoice management / HR records / etc.) are not in this repo — they belong on the downstream consumer side per the channel philosophy.

Prerequisites

Requirement	Minimum version	Notes
.NET SDK	10.0
Node.js	20	Required for the Angular frontend (Angular 21 needs Node 20.19+ / 22.12+)
SQL Server	2019+	LocalDB works for development; production runs full SQL Server
Docker Desktop	any recent	Optional but recommended — runs the PaddleOCR sidecar and the local OpenTelemetry dashboard

Getting started (local development)

1. Start the PaddleOCR sidecar

PaddleOCR is the default OCR provider. It runs as a Docker container:

cd host
docker compose up -d paddleocr

First run downloads ~600 MB of model weights and takes 30–60 seconds. Subsequent starts are instant.

2. Configure the database

Create host/src/appsettings.Development.json with your local SQL Server connection string:

{
  "Serilog": { "MinimumLevel": { "Default": "Debug" } },
  "ConnectionStrings": {
    "Default": "Server=YOUR_DB_SERVER;Database=Paperbase-Dev;User ID=YOUR_USER;Password=YOUR_PASSWORD;TrustServerCertificate=true"
  },
  "StringEncryption": {
    "DefaultPassPhrase": "any-random-string-here"
  }
}

This file is git-ignored. In Development mode, the application automatically generates temporary OpenIddict certificates — no .pfx file is needed. For LocalDB, the committed appsettings.json default (Server=(LocalDb)\MSSQLLocalDB;...) already works without any override.

3. Install client-side libraries

cd host/src
abp install-libs

4. Run the backend

cd host/src
dotnet run

API: https://localhost:44348. Swagger: https://localhost:44348/swagger.

5. Install frontend dependencies and run Angular

cd host/angular
npm install
npm start

SPA: http://localhost:4200. Default seeded credentials: admin / 1q2w3E*.

Choosing an OCR provider

Paperbase ships two OCR providers — local PaddleOCR (default, CPU, no network) and cloud Azure Document Intelligence. PaddleOCR is the zero-config default for development; Azure DI is the recommended production option when data is allowed to leave the network.

Full selection guidance, configuration, and resource footprint: see docs/text-extraction.md.

Deploying to production

For database connection strings, OpenIddict signing certificate, string-encryption key, and the Docker layout, see docs/deployment.md. For per-release smoke tests, see docs/deployment-checklist.md.

Documentation

Feature docs (start here for any specific topic):

• Local development setup — prerequisites, Docker sidecars, configuration, troubleshooting
• Text extraction — Markdown-first contract, PaddleOCR / Azure DI configuration
• Classification — document-type pipeline and prompt tuning
• Reprocessing — bulk re-run of classification / field extraction over existing documents after a config change
• AI provider — provider wiring for the two keyed chat clients (title generator + structured)
• Observability — OpenTelemetry pipeline, aspire-dashboard for local dev, switching OTLP backends
• Pipeline runs — run history and review-UI payloads
• Deployment — DB, certificate, Docker
• Deployment checklist — per-release smoke tests

External references:

• Angular Application
• ABP Framework Documentation
• Application (Single Layer) Startup Template
• Configuring OpenIddict for Production